Ovarian cancer's deadly nature can be attributed to its diagnosis at late stages of disease when survival is poor. Though CA125 was identified more than thirty years ago, recent studies have shown that it remains the single best ovarian cancer screening biomarker. Variability in CA125 in healthy women and low values in 20 percent of women with ovarian cancer limit its performance and utility for population screening. Here we propose to develop personalized cutpoints for CA125 based on individual characteristics to improve the performance of CA125 as a screening biomarker using data and samples from four large established studies. First, we will build a model with variables that predict CA125 levels in more than 25,000 healthy women from the Prostate Lung Colorectal and Ovarian Cancer Screening trial (PLCO) with validation in more than 4,000 women from the New England Case Control Study (NECC), Nurses' Health Study (NHS), and European Prospective Investigation into Nutrition and Cancer (EPIC). Then, we will create personalized CA125 cutpoints based on each woman's individual characteristics that will determine what the normal CA125 threshold should be for that woman. We will evaluate the ability of personalized CA125 cutpoints versus a single cutpoint (35 U/mL) to distinguish cases from controls and ultimately determine whether addition of personalized CA125 to established risk prediction models improves their performance. We hypothesize that by reducing the background noise introduced by exposures that elevate or lower CA125, these personalized cutpoints will improve CA125 as an ovarian cancer screening biomarker for population based ovarian cancer screening. Our team, which includes expertise in ovarian cancer, biomarkers, gynecologic oncology, epidemiology, and biostatistics, is poised to make meaningful advances in reducing the morbidity and mortality of ovarian cancer by tailoring CA125 to each woman's personal characteristics using the best possible data for ovarian cancer screening.